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    <h2>Welcome to transsys</h2>
    <p>
        The <tt>transsys</tt> system is a modelling framework which is sufficiently generic to integrate several methods for modelling gene regulatory systems, facilitates interfacing with
        models of growth process and allows formulation of concise and expressive models to
        facilitate scientific communication. It is developed by <a href="http://www.cmp.uea.ac.uk/people/jtk">Jan
            T. Kim</a>, initially at the <a href="/">Institute for
            Neuro- and Bioinformatics</a>, and now at the
        <a href="http://www.cmp.uea.ac.uk/">School of Computing Sciences</a>
        of the <a href="http://www.uea.ac.uk/">University of East Anglia</a>.

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    <p>
        The core of this framework is the formal language <tt>transsys</tt> which representing GRNs. The modelling process with the <tt>transsys</tt> system is divided into two stages. The first stage is building <tt>transsys</tt> models in the <tt>transsys</tt> language. The source code which describes a GRN is called a <tt>transsys</tt> program. Each <tt>transsys</tt> program contains the definitions of transcription factors as well as genes involved in the GRN. The transcription factors are declared by factor blocks containing factor's name as an attribute and two possible properties which are decay rate and diffusion rate. The decay rate property specifies the amount of a factor decays in each time step. The diffusion rate property (diffusibility) defines the ratio of a factor is distributed among the neighbouring <tt>transsys</tt> instances. Genes are described in <tt>transsys</tt> program by comprising two blocks of code, a regulatory block and a product block. The former (also known as promoter block) contains a list of statements that define the conditions of activation or repression of this gene. The latter simply describes the factor which is encoded by this gene.
    </p><p>
The second stage is simulating the working process of GRNs. As mentioned above, the <tt>transsys</tt> framework employs the object oriented (OO) approach. Once <tt>transsys</tt> model is defined in <tt>transsys</tt> program; it is considered as a class in OO programming, which means that instances of this model can be created. In a <tt>transsys</tt> instance, there is a map storing the concentration of the factors( expression level)in the networks. The value of each element in this array then is updated in a procedure based on the defined factors and gene properties. The procedure calculates the factor concentrations in the next time step from the concentrations on the current time step.
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